NDLI: Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.

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Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.

Content Provider	Europe PMC
Author	Wei, Wei Southern, Joshua Zhu, Kexuan Li, Yefeng Cordeiro, Maria Francesca Veselkov, Kirill
Abstract	Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions.
Journal	Scientific Reports [Sci Rep]
Volume Number	13
DOI	10.1038/s41598-023-35414-y
PubMed Central reference number	PMC10203346
Issue Number	1
PubMed reference number	37217770
e-ISSN	20452322
Language	English
Publisher	Nature Publishing Group
Publisher Date	2023-05-22
Publisher Place	London
Access Restriction	Open
Subject Keyword	Macular degeneration Computational models
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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